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Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

Nikita Joshi, Sarika Jain, Amit Agarwal
Copyright: © 2020 |Volume: 13 |Issue: 4 |Pages: 18
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781799805489|DOI: 10.4018/JITR.2020100102
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MLA

Joshi, Nikita, et al. "Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images." JITR vol.13, no.4 2020: pp.14-31. http://doi.org/10.4018/JITR.2020100102

APA

Joshi, N., Jain, S., & Agarwal, A. (2020). Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images. Journal of Information Technology Research (JITR), 13(4), 14-31. http://doi.org/10.4018/JITR.2020100102

Chicago

Joshi, Nikita, Sarika Jain, and Amit Agarwal. "Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images," Journal of Information Technology Research (JITR) 13, no.4: 14-31. http://doi.org/10.4018/JITR.2020100102

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Abstract

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

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